Work machine, control device and control program

ABSTRACT

A work machine having an autonomous travel function may include: a cutting section that cuts a work target of the work machine; an image-capturing section that captures an image of the work target cut by the cutting section; and a judging section that judges a state of the cutting section based on the image captured by the image-capturing section. The judging section may judge whether maintenance of or a check on the cutting section is necessary or not based on a result of judgment about the state of the cutting section.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application of International Application No.PCT/JP2017/045000 filed on Dec. 14, 2017, which claims priority toJapanese Patent Application No. 2016-257002 filed in JP on Dec. 28,2016, the contents of each of which are incorporated herein byreference.

BACKGROUND 1. Technical Field

The present invention relates to a work machine, control device andprogram.

2. Related Art

In recent years, lawn mowers, cleaners and the like that runautonomously to work have been developed (please see Patent Document 1or 2, for example).

PATENT DOCUMENTS

-   [Patent Document 1] Japanese Patent Application Publication No.    2016-185099-   [Patent Document 2] Japanese Patent Application Publication No.    2013-223531

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 schematically shows one example of the internal configuration ofa work machine 100.

FIG. 2 schematically shows one example of the system configuration of agarden managing system 200.

FIG. 3 schematically shows one example of the internal configuration ofa managing server 210.

FIG. 4 schematically shows one example of the internal configuration ofan image analyzing section 320.

FIG. 5 schematically shows one example of the internal configuration ofan information storage section 322.

FIG. 6 schematically shows one example of the internal configuration ofa lawn mower 230.

FIG. 7 schematically shows one example of the internal configuration ofa control unit 680.

FIG. 8 schematically shows another example of the internal configurationof the control unit 680.

FIG. 9 schematically shows one example of the system configuration of asprinkling device 240.

FIG. 10 schematically shows another example of the system configurationof the sprinkling device 240.

FIG. 11 schematically shows one example of information processing at theimage analyzing section 320.

FIG. 12 schematically shows one example of a data table 1200.

FIG. 13 schematically shows one example of a data table 1300.

FIG. 14 schematically shows one example of a data table 1400.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, (some) embodiment(s) of the present invention will bedescribed. The embodiment(s) do(es) not limit the invention according tothe claims, and all the combinations of the features described in theembodiment(s) are not necessarily essential to means provided by aspectsof the invention. Identical or similar portions in figures are givenidentical reference numbers, and the same explanation is omitted in somecases.

[Outline of Work Machine 100]

FIG. 1 schematically shows one example of the internal configuration ofa work machine 100. In the present embodiment, the work machine 100includes an autonomous travelling section 110, a cutting section 120, animage-capturing section 130 and a control device 140. The control device140 includes, for example, a judging section 142 and a control section144.

In the present embodiment, the work machine 100 executes various typesof works. Examples of the works may include pruning, lawn mowing, grassmowing, watering, fertilization, cleaning, transportation, monitoring,security, guard and the like. The work machine 100 may be a travellingbody having an autonomous travel function. Thereby, the work machine 100for example can travel by automatic operation by a computer mounted onthe work machine 100. The work machine 100 may travel by remotemanipulation of a user. The work machine 100 may be a travelling body torun, a travelling body to fly, or a travelling body to travel in or onwater.

In the present embodiment, the autonomous travelling section 110 causesthe work machine 100 to travel. The autonomous travelling section 110may cause the work machine 100 to travel autonomously in accordance withcontrol by the control device 140. The autonomous travelling section 110may include a thrust generating section (not illustrated in figures)such as a wheel or a propeller, and a driving section (not illustratedin figures) that drives the thrust generating section. Examples of thedriving section may include an engine, a motor, a prime mover and thelike. The autonomous travelling section 110 may include a positionalinformation acquiring section (not illustrated in figures) that acquirespositional information indicating a position of the work machine 100.Examples of the positional information acquiring section may include aGPS signal receiver, a beacon signal receiver, a radio field intensitymeasuring machine, a millimeter wave sensor, a magnetic sensor, acamera, an infrared camera, a microphone, an ultrasonic wave sensor andthe like.

In the present embodiment, the cutting section 120 cuts a work target ofthe work machine 100. For example, if the work machine 100 is a lawnmower or grass mower, the work target is plants such as lawn grasses orweeds. The cutting section 120 for example includes a cutting blade, anda motor to rotate the cutting blade. Examples of the cutting blade mayinclude a chip saw, a nylon cutter, a metal blade and the like.

In the present embodiment, the image-capturing section 130 captures animage of the space around the work machine 100. For example, theimage-capturing section 130 captures an image of a work target cut bythe cutting section 120. The image-capturing section 130 may capture animage of a work target just cut while the work machine 100 is working.The image-capturing section 130 may transmit image data of a work targetto the judging section 142. The image-capturing section 130 maytransmit, to the judging section 142 and in association with each other,data of an image of a work target and positional information indicatinga position where the image of the work target was captured. Theabove-mentioned image may be a still image or a moving image.

The work machine 100 may have a single image-capturing section 130 or aplurality of image-capturing sections 130. Each image-capturing section130 among the one or more image-capturing sections 130 may have a singleimage sensor or a plurality of image sensors. An image-capturing section130 may have a 360-degree angle camera. An image-capturing section 130may adjust an image-capturing condition in accordance with aninstruction of the control device 140. If an image-capturing section 130has a plurality of image sensors, the image-capturing section 130 mayadjust an image-capturing condition for each image sensor in accordancewith an instruction of the control device 140. An image-capturingsection 130 may be used exclusively for a use of capturing an image of awork target or may be used for a plurality of uses.

In the present embodiment, the control device 140 controls the workmachine 100. In one embodiment, the control device 140 may controlautonomous travel of the work machine 100. The control device 140 maycontrol the autonomous travelling section 110 to cause the work machine100 to travel. For example, the control device 140 controls at least oneof the travel speed, travel direction and travel route of the workmachine 100. In another embodiment, the control device 140 may controlwork of the work machine 100. For example, the control device 140controls at least one of the work type, work strength and work scheduleof the work machine 100.

In the present embodiment, the judging section 142 judges the state ofthe cutting section 120 based on an image captured by an image-capturingsection 130. For example, the judging section 142 receives, as an input,data of an image to be a target of judgment, and outputs, as a result ofjudgment, a parameter indicating the state of the cutting section 120.The image to be a target of judgment is, for example, an image of aplurality of lawn grasses growing in a particular area.

The judging section 142 may transmit a result of judgment to the controlsection 144. The judging section 142 may transmit, to the controlsection 144 and in association with each other, information indicating aresult of judgment and information about a position where an imageutilized in the judgment was captured. The information about a positionwhere an image was captured may be positional information indicating aposition of an image-capturing section 130 that captured the image orpositional information indicating a position of an object of the image.If an image-capturing section 130 is mounted on a travelling body,positional information indicating a position of the image-capturingsection 130 may be positional information indicating a position of thetravelling body.

The judging section 142 may (i) judge the state of the cutting section120 based on a predetermined determination criterion, or (ii) judge thestate of the cutting section 120 utilizing a learning model obtainedthrough machine learning. The above-mentioned determination criterionmay be information in which one or more factors (which may be sometimesreferred to as factors to consider), conditions about respective factorsto consider, and parameters indicating the state of the cutting section120 are associated with each other.

Examples of the factors to consider for judging the state of the cuttingsection 120 may include (i) the type of lawn grasses, (ii) the number ordensity of lawn grasses, (iii) the appearance of cut portions of lawngrasses, (iv) a specification of the cutting section 120, and the like.Examples of the appearance of cut surfaces of lawn grasses may include(i) at least one of the shape, hue and luster of cut surfaces, (ii)presence or absence, or degree of burrs, (iii) the presence or absence,or degree of liquid droplets, and the like.

For example, if the cutting performance of a blade is not good, some ofveins of a lawn grass project from a cut surface of the lawn grass. Theveins projecting from a cut surface are often white. Because of this, ifthe cutting performance of a blade is not good, the cut surface of alawn grass is not flat, and also as compared with a case where thecutting performance of the blade is good, the number of white dots in animage increases.

Based on what kind of determination criterion the state of the cuttingsection 120 is judged may be decided by a user or administrator, or maybe decided through machine learning. Based on what kind of determinationcriterion the state of the cutting section 120 is judged may be decidedfor each type of lawn grasses, or may be decided for each specificationof a lawn mower. A threshold for deciding whether or not a target ofjudgment matches a condition about each factor to consider may bedecided by a user or administrator, or may be decided through machinelearning. The above-mentioned threshold may be decided for each type ofa work target or may be decided for each specification of the cuttingsection 120.

The machine learning may be supervised learning, unsupervised learning,or reinforcement learning. In the learning process, learning techniquesusing a neural network technology, deep-learning technology or the likemay be used.

For example, first, a database about features of images of a cut worktarget is created for each state of the cutting section 120. Theabove-mentioned database may be created utilizing images of a worktarget cut by a single cutting section 120, or may be created utilizingimages of a work target cut by a plurality of cutting sections 120. Theabove-mentioned database may be created utilizing machine learning. Theabove-mentioned database may be further subdivided.

According to one embodiment, the database for each state of the cuttingsection 120 includes a plurality of databases created for respectivetypes of a work target (which are, for example, types of lawn grasses).According to another embodiment, the database for each state of thecutting section 120 includes a plurality of databases created forrespective growth states of a work target (which are, for example, thewater-supply state of lawn grasses, the density of lawn grasses, and thelike).

Next, the judging section 142 receives, from an image-capturing section130, data of an image captured by the image-capturing section 130. Inone embodiment, the judging section 142 compares a feature of the imagecaptured by the image-capturing section 130 and features of images thatare associated with respective states of the cutting section 120 in adatabase, and decides the state of the cutting section 120 matching theimage captured by the image-capturing section 130. In a judgment processof the judging section 142, a known image recognition technique may beutilized, or an image recognition technique to be newly developed in thefuture may be utilized. In an image recognition process, an imagerecognition technique utilizing machine learning may be utilized.

In another embodiment, the judging section 142 for example analyzes animage captured by an image-capturing section 130 to decide the type of awork target. The judging section 142 selects a learning modelcorresponding to the type of a work target from one or more learningmodels generated through machine learning performed in advance. Thejudging section 142 decides the state of the cutting section 120matching the image captured by the image-capturing section 130 utilizingthe selected learning model.

Examples of the state of the cutting section 120 may include (i) thecutting performance of the cutting section 120, (ii) whether maintenanceof or a check on the cutting section 120 is necessary or not, (iii)recommended timing of maintenance of or a check on the cutting section120, or time left until the timing, and the like. The state of thecutting section 120 may be evaluated by consecutive numerical values, ormay be evaluated stepwise using a plurality of steps.

For example, the cutting performance of the cutting section 120 isexpressed by a parameter obtained by converting the current cuttingperformance into a numerical value assuming that the cutting performanceof a brand-new cutting section is 100%. The performance of the cuttingsection 120 may be expressed by a parameter for two-step evaluationconsisting of “good” and “bad”, or a parameter for four-step evaluationconsisting of “excellent”, “good”, “acceptable” and “not acceptable”.Likewise, time left until the timing of maintenance or a check may beexpressed by a parameter represented by consecutive numerical valuessuch as “the number of months”, “the number of days” or “the number ofhours”, or may be expressed by a parameter for three-step evaluationconsisting of “there is sufficient time left until timing ofreplacement”, “there is relatively sufficient time left until timing ofreplacement”, or “it is about timing of replacement”. The number ofcategories of evaluation is not limited to the above-mentioned example.

In the present embodiment, the control section 144 controls the workmachine 100. The control section 144 may control the work machine 100based on a result of judgment by the judging section 142. For example,the control section 144 controls the work machine 100 according to thestate of the cutting section 120. In one embodiment, the control section144 controls travel of the work machine 100 according to the state ofthe cutting section 120. For example, the control section 144 controlsat least one of the travel speed, travel direction and travel route ofthe work machine 100 according to the state of the cutting section 120.In another embodiment, the control section 140 controls work of the workmachine 100 according to the state of the cutting section 120. Forexample, the control section 144 controls the cutting strength of thecutting section 120 by adjusting the rotational speed of a cutting bladeor the like according to the state of the cutting section 120.

According to the present embodiment, an image-capturing section 130 isdisposed in the work machine 100. Because of this, the image-capturingsection 130 can capture an image of a work target in the vicinity of thework target. Thereby, the image-capturing section 130 can capture animage of the appearance of the work target in more detail. As a result,a high level judgment process becomes possible at the judging section142.

For example, if a work target is lawn grasses, according to oneembodiment, the judging section 142 analyzes an image captured by theimage-capturing section 130 to recognize the shape of each and everylawn grass. The judging section 142 may recognize an end portion of eachlawn grass based on the shape of each lawn grass.

In another embodiment, the judging section 142 analyzes an imagecaptured by an image-capturing section 130 to extract a region in theimage where many end portions of a plurality of lawn grasses areincluded. For example, if the image-capturing section 130 is disposed ata position that allows it to laterally capture an image of lawn grassescut by the cutting section 120, the image-capturing section 130 canacquire an image in which a plurality of lawn grasses are capturedlaterally. The judging section 142 divides the above-mentioned imageinto a plurality of images in the vertical direction or a direction inwhich the lawn grasses grow. The judging section 142 extracts, from aplurality of images obtained by division, an image that is likely toinclude end portions of lawn grasses. For example, the judging section142 (i) divides, in the vertical direction into two images, an image inwhich a plurality of lawn grasses are captured laterally, and (ii)extracts a top image as an image that is likely to include end portionsof lawn grasses. Thereby, the judging section 142 can recognize the endportions of the lawn grasses.

According to the present embodiment, the judging section 142 recognizesend portions of lawn grasses. Thereby, the judging section 142 canrecognize a feature of cut surfaces of the lawn grasses. As a result,the judging section 142 can judge the state of the cutting section 120based on the feature of the cut surfaces of the lawn grasses. Forexample, the judging section 142 can judge the state of the cuttingsection 120 utilizing at least one feature selected from a groupconsisting of: (i) the shape, hue and luster of a cut surface of a lawngrass; (ii) how much sap or water drips out of a cut surface of a lawngrass; (iii) the length of a lawn grass after being cut; and (iv)regarding the above-mentioned features i to iii, (a) presence or absenceof a lawn grass having a particular feature, or (b) the proportion ofthe number or area of lawn grasses having the particular feature to thenumber or area of observed lawn grasses.

Also, according to the present embodiment, an image-capturing section130 can capture an image of a work target immediately after work whilethe work machine 100 is travelling. Thereby, the state of the worktarget immediately after work can be fed back to the work of the workmachine 100. For example, if the work quality does not satisfy apredetermined criterion, the control section 144 lowers the travel speedof the work machine 100, increase the work strength, and so on in orderto improve the work quality. For example, the work strength can beincreased by increasing the rotational speed of a cutting blade. Also,if the work efficiency fluctuates due to the above-mentioned change, thecontrol section 144 can also change the travel route of the work machine100, change the charging schedule of the work machine 100, and so on.

[Specific Configuration of Each Section of Work Machine 100]

Each section of the work machine 100 may be realized by hardware,software, or hardware and software. If at least some of componentsconstituting the work machine 100 are realized by software, thecomponents realized by the software may be realized by activating, in aninformation processing device having a general configuration, softwareor a program stipulating operations about the components.

The above-mentioned information processing device may include: (i) adata processing device having processors such as a CPU or a GPU, a ROM,a RAM, a communication interface and the like, (ii) input devices suchas a keyboard, touch panel, camera, microphone, various types of sensorsor GPS receiver, (iii) output devices such as a display device, aspeaker or a vibration device, and (iv) storage devices (includingexternal storage devices) such as a memory or a HDD. In theabove-mentioned information processing device, the above-mentioned dataprocessing device or storage devices may store the above-mentionedsoftware or program. Upon being executed by a processor, theabove-mentioned software or program causes the above-mentionedinformation processing device to execute operations stipulated by thesoftware or program. The above-mentioned software or program may bestored in a non-transitory computer-readable recording medium.

The above-mentioned software or program may be a control program forcontrolling the work machine 100. The control program may be a programfor causing a computer to execute a judgment procedure of judging thestate of the cutting section 120 based on an image captured by animage-capturing section 130 and a control procedure of controlling thework machine 100 based on a result of judgment in the judgmentprocedure. The above-mentioned computer may be a computer mounted on thework machine 100 or may be a computer that controls the work machine 100via a communication network.

[Outline of Garden Managing System 200]

FIG. 2 schematically shows one example of the system configuration of agarden managing system 200. The garden managing system 200 manages agarden 30. For example, the garden managing system 200 manages thevegetation state of the garden 30. The garden managing system 200 maymanage growth of plants cultivated in the garden 30. In the presentembodiment, the garden managing system 200 includes a managing server210, a monitoring camera 220, a lawn mower 230 and a sprinkling device240. The sprinkling device 240 for example has a sprinkler 242 and awater-supply control section 244.

The garden managing system 200 may be one example of a water-supplysystem, information processing system or control device. The managingserver 210 may be one example of a water-supply system, informationprocessing device or control device. The monitoring camera 220 may beone example of an image-capturing section or image acquiring section.The lawn mower 230 may be one example of a work machine, water-supplysystem, information processing system, control device, image-capturingsection or image acquiring section. The sprinkling device 240 may be oneexample of a water-supply section. The sprinkler 242 may be one exampleof a water-supply section. The water-supply control section 244 may beone example of a water-supply section.

In the present embodiment, each section of the garden managing system200 can transmit and receive information to and from each other via acommunication network 10. Each section of the garden managing system 200may transmit and receive information to and from a user terminal 20 viathe communication network 10. In the present embodiment, the monitoringcamera 220, the lawn mower 230 and the sprinkling device 240 aredisposed inside or around the garden 30.

In the present embodiment, the communication network 10 may be a wiredcommunication transmission path, a wireless communication transmissionpath, or a combination of a wireless communication transmission path anda wired communication transmission path. The communication network 10may include a wireless packet communication network, the Internet, a P2Pnetwork, a private line, a VPN, an electric power line communicationline and the like. The communication network 10: (i) may include amobile communication network such as a mobile phone line network; and(ii) may include a wireless communication network such as a wireless MAN(for example, WiMAX (registered trademark)), a wireless LAN (forexample, WiFi (registered trademark)), Bluetooth (registered trademark),Zigbee (registered trademark) or NFC (Near Field Communication).

In the present embodiment, the user terminal 20 is a communicationterminal that a user of the garden 30, the garden managing system 200 orthe lawn mower 230 utilizes, and details thereof are not particularlylimited. Examples of the user terminal 20 may include a personalcomputer, mobile terminal and the like. Examples of the mobile terminalmay include a mobile phone, a smartphone, a PDA, a tablet, a notebookcomputer or laptop computer, a wearable computer and the like.

In the present embodiment, the managing server 210 manages themonitoring camera 220, the lawn mower 230 and the sprinkling device 240.For example, the managing server 210 collects information about thegarden 30 from at least one of the monitoring camera 220, the lawn mower230 and the sprinkling device 240. The managing server 210 may generateinformation indicating a geographical distribution (which may besometimes referred to as map information) of features of the garden 30.The managing server 210 may manage the state of at least one of themonitoring camera 220, lawn mower 230 and sprinkling device 240. Themanaging server 210 may control operation of at least one of themonitoring camera 220, the lawn mower 230 and the sprinkling device 240.

In the present embodiment, the monitoring camera 220 monitors the garden30. For example, the monitoring camera 220 captures an image of a workarea of the lawn mower 230. The monitoring camera 220 may capture animage of the lawn mower 230 while it is working. The monitoring camera220 may capture an image of a work target of the lawn mower 230. Themonitoring camera 220 may capture an image of lawn grasses presentaround the lawn mower 230 while it is working. The monitoring camera 220may capture an image of lawn grasses present in the forward direction interms of a course of the lawn mower 230. The monitoring camera 220 maycapture an image of lawn grasses present in a region that the lawn mower230 passed through. Lawn grasses may be one example of a work target ofthe lawn mower 230. Lawn grasses may be one example of an object ofimage data.

In the present embodiment, the lawn mower 230 has an autonomous travelfunction. The lawn mower 230 cuts lawn grasses while it is runningautonomously in the garden 30. To cut lawn grasses (which may besometimes referred to as lawn mowing) may be one example of a work ofthe lawn mower 230, and lawn grasses may be one example of a work targetof the lawn mower 230. Lawn grasses may be one example of plants growingin the garden 30.

In one embodiment, the lawn mower 230 has a communication function. Thelawn mower 230 for example transmits and receives information to andfrom at least one of the managing server 210, the monitoring camera 220and the sprinkling device 240 via the communication network 10. Forexample, the lawn mower 230 travels in the garden 30, performs lawnmowing, and so on based on an instruction from the managing server 210.Lawn mower 230 may collect information about the garden 30 while it istravelling or working, and transmit the information to the managingserver 210. The information about the garden 30 may be information aboutan ecological system in the garden 30. The information about the garden30 may be information about vegetation of the garden 30. The informationabout the garden 30 may be information about the state of lawn grasses.

In another embodiment, the lawn mower 230 may have an image-capturingdevice mounted thereon. The lawn mower 230 may capture an image of lawngrasses present around the lawn mower 230 using the image-capturingdevice. The lawn mower 230 may capture an image of lawn grasses presentin the forward direction in terms of a course of the lawn mower 230.Thereby, close-up image-capturing of lawn grasses that the lawn mower230 is about to cut at the moment becomes possible. The lawn mower 230may capture an image of lawn grasses present in a region that the lawnmower 230 passed through. Thereby, close-up image-capturing of lawngrasses cut by the lawn mower 230 becomes possible.

The lawn mower 230 may execute various types of judgment processes basedon an image of lawn grasses captured by an image-capturing device. Forexample, the lawn mower 230 judges the state of the lawn mower 230 basedon an image of lawn grasses captured by the image-capturing device. Thelawn mower 230 may control at least either travel or work of the lawnmower 230 based on an image of lawn grasses captured by theimage-capturing device. The lawn mower 230 may judge whether or notwater has been supplied to the garden 30 based on an image of lawngrasses captured by the image-capturing device.

In the present embodiment, the sprinkling device 240 supplies water toplants growing in the garden 30. The sprinkling device 240 may supplywater to plants based on a decision by the managing server 210 or lawnmower 230. The sprinkling device 240 may be installed in the garden 30or may be mounted on the lawn mower 230.

In the present embodiment, the sprinkler 242 sprinkles water. Thesprinkler 242 may sprinkle water containing fertilizer components. Inthe present embodiment, the water-supply control section 244 controlsthe amount of water to be supplied to the sprinkler 242. For example,the water-supply control section 244 receives, from the managing server210 or lawn mower 230, an instruction about water-supply. Thewater-supply control section 244 controls the start or stop ofwater-supply based on the above-mentioned instruction. The water-supplycontrol section 244 may adjust the amount of water-supply based on theabove-mentioned instruction.

[Specific Configuration of Each Section of Garden Managing System 200]

Each section of the garden managing system 200 may be realized byhardware, software, or hardware and software. Each section of the gardenmanaging system 200 may be, at least partially, realized by a singleserver or a plurality of servers. Each section of the garden managingsystem 200 may be, at least partially, realized on a virtual server orcloud system. Each section of the garden managing system 200 may be, atleast partially, realized by a personal computer or mobile terminal.Examples of the mobile terminal may include a mobile phone, asmartphone, a PDA, a tablet, a notebook computer or laptop computer, awearable computer and the like. The garden managing system 200 may storeinformation utilizing a distributed ledger technology or distributednetwork such as a blockchain.

If at least some of components constituting the garden managing system200 are realized by software, the components realized by the softwaremay be realized by activating, in an information processing devicehaving a general configuration, software or a program stipulatingoperations about the components.

The above-mentioned information processing device may include: (i) adata processing device having processors such as a CPU or a GPU, a ROM,a RAM, a communication interface and the like, (ii) input devices suchas a keyboard, touch panel, camera, microphone, various types of sensorsor GPS receiver, (iii) output devices such as a display device, aspeaker or a vibration device, and (iv) storage devices (includingexternal storage devices) such as a memory or a HDD. In theabove-mentioned information processing device, the above-mentioned dataprocessing device or storage devices may store the above-mentionedsoftware or program. Upon being executed by a processor, theabove-mentioned software or program causes the above-mentionedinformation processing device to execute operations stipulated by thesoftware or program. The above-mentioned software or program may bestored in a non-transitory computer-readable recording medium.

FIG. 3 schematically shows one example of the internal configuration ofthe managing server 210. In the present embodiment, the managing server210 includes a receiving section 310, an image analyzing section 320, aninformation storage section 322, an instruction generating section 330and a transmitting section 340. Each section of the managing server 210may transmit and receive information to and from each other, indirections not limited by arrows in the figure.

The receiving section 310 may be one example of an image acquiringsection. The image analyzing section 320 may be one example of aninformation processing device or control device. The image analyzingsection 320 may be one example of an image acquiring section, positionalinformation acquiring section, judging section, form recognizingsection, deciding section or control parameter deciding section. Theinstruction generating section 330 may be one example of a controlsection, travel control section, work control section, deciding sectionor control parameter deciding section. The transmitting section 340 maybe one example of a notifying section.

In the present embodiment, the receiving section 310 acquiresinformation transmitted by at least one of the user terminal 20, themonitoring camera 220, the lawn mower 230 and the sprinkling device 240.For example, the receiving section 310 receives image data from at leasteither the monitoring camera 220 or the lawn mower 230. Thereby, themanaging server 210 can acquire data of an image captured by themonitoring camera or data of an image captured by an image-capturingdevice mounted on the lawn mower 230. The above-mentioned image may be astill image or moving image. The above-mentioned image data may be imagedata of a work target (for example, plants such as lawn grasses orweeds) of the lawn mower 230. The receiving section 310 may receivepositional information associated with the above-mentioned image data.The receiving section 310 transmits the above-mentioned image data tothe image analyzing section 320. If positional information is associatedwith the above-mentioned image data, the receiving section 310 maytransmit the image data and the positional information to the imageanalyzing section 320.

In the present embodiment, the image analyzing section 320 analyzesimage data. The image analyzing section 320 may analyze image datautilizing an image recognition technique. The above-mentioned imagerecognition technique may be a known image recognition technique or maybe an image recognition technique to be newly developed in the future.In the above-mentioned image recognition technique, a machine-learningtechnique or deep-learning technique may be utilized.

For example, the image analyzing section 320 acquires, from thereceiving section 310, image data of an image to be a target ofanalysis. The image analyzing section 320 analyzes the above-mentionedimage data, and generates at least either (i) various types ofparameters about the lawn mower 230 or garden 30, or (ii) various typesof map information about the garden 30. Examples of the map informationmay include a geographical distribution of various types of parametersin the garden 30, a vegetation distribution in the garden 30, and thelike.

Examples of the various types of parameters may include (i) a parameterindicating the state of the lawn mower 230 (which may be sometimesreferred to as a state parameter), (ii) a parameter for controlling thelawn mower 230 (which may be sometimes referred to as a controlparameter), (iii) a parameter about whether water-supply to plants inthe garden 30 is necessary or not, or the level of water content in amedium of the plants (which may be sometimes referred to as awater-supply parameter), and the like. The state parameter may be aparameter indicating the state of a blade to cut lawn grasses. The blademay be one example of a cutting section.

The state parameter may be a parameter indicating (i) the cuttingperformance of a blade of the lawn mower 230, (ii) whether maintenanceof or a check on the blade is necessary or not, (iii) recommended timingof maintenance of or a check on the blade, or time left until thetiming, or the like. Examples of the maintenance may include polish,repair, replacement and the like. Examples of the control parameter mayinclude (i) a parameter for controlling travel of the lawn mower 230,(ii) a parameter for controlling work of the lawn mower 230, and thelike.

The parameter about the level of water content in a medium may be thewater content in the medium. Examples of the parameter about whetherwater-supply is necessary or not may include information indicating thatwater-supply is necessary, information indicating that water-supply isunnecessary, information indicating the amount of water that should besupplied to a medium (which may be sometimes referred to as the amountof water-supply), and the like. The amount of water-supply may be awater supply amount per time or a water supply amount per area, volumeor weight of a medium. If the amount of water-supply is 0 or if theamount of water-supply is smaller than a predetermined value,information indicating that water-supply is unnecessary may begenerated. If the amount of water-supply exceeds the predeterminedvalue, information indicating that water-supply is necessary may begenerated.

The image analyzing section 320 may output a result of analysis of imagedata to the instruction generating section 330 or transmitting section340. In one embodiment, the image analyzing section 320 transmits atleast one of the state parameter, the control parameter and thewater-supply parameter to the instruction generating section 330. Theimage analyzing section 320 transmits the above-mentioned parameter tothe instruction generating section 330 in a map information format. Inanother embodiment, if at least one of the state parameter, the controlparameter and the water-supply parameter satisfies a predeterminedcondition, the image analyzing section 320 generates a message to notifythe user terminal 20 of such a fact. The image analyzing section 320outputs the above-mentioned message to the transmitting section 340.

In the present embodiment, the information storage section 322 storesvarious types of information. The information storage section 322 maystore information to be utilized in image analysis processing at theimage analyzing section 320. The information storage section 322 maystore a result of analysis by the image analyzing section 320. Forexample, the information storage section 322 stores learning data formachine learning of the image analyzing section 320. Also, theinformation storage section 322 stores a learning model constructedthrough machine learning of the image analyzing section 320. Theinformation storage section 322 may store image data acquired by thereceiving section 310, various types of parameters and various types ofmaps generated by the image analyzing section 320, and the like.

In the present embodiment, the instruction generating section 330generates an instruction to at least either the lawn mower 230 or thesprinkling device 240. For example, the instruction generating section330 receives information indicating a result of analysis by the imageanalyzing section 320 from the image analyzing section 320, andgenerates an instruction to at least either the lawn mower 230 or thesprinkling device 240 based on the result of analysis. The instructiongenerating section 330 may generate an instruction based on at least oneparameter, may generate an instruction based on at least one piece ofmap information, and may generate an instruction based on at least oneparameter and at least one piece of map information.

According to one embodiment, if a state parameter included in theabove-mentioned result of analysis satisfies a predetermined condition,the instruction generating section 330 generates an instruction fordisplaying, on a user interface of the lawn mower 230, a messagecorresponding to the above-mentioned condition. For example, if thestate parameter indicates that maintenance of or a check on a blade isnecessary, the instruction generating section 330 generates aninstruction for displaying, on the user interface of the lawn mower 230,a message indicating that maintenance of or a check on the blade isrecommended.

According to another embodiment, if the above-mentioned result ofanalysis includes a control parameter or if a control parameter includedin the above-mentioned result of analysis satisfies a predeterminedcondition, the instruction generating section 330 generates aninstruction for controlling the lawn mower 230. According to stillanother embodiment, if a water-supply parameter included in theabove-mentioned result of analysis satisfies a predetermined condition,the instruction generating section 330 generates an instruction forcontrolling the sprinkling device 240.

In the present embodiment, the transmitting section 340 transmitsinformation to at least one of the user terminal 20, the lawn mower 230and the sprinkling device 240. According to one embodiment, thetransmitting section 340 transmits a message generated by the imageanalyzing section 320 to at least one of the user terminal 20, the lawnmower 230 and the sprinkling device 240. According to anotherembodiment, the transmitting section 340 transmits an instructiongenerated by the instruction generating section 330 to at least one ofthe user terminal 20, the lawn mower 230 and the sprinkling device 240.Thereby, a result of analysis by the image analyzing section 320 can benotified to a user, transmitted to the lawn mower 230 or sprinklingdevice 240, and so on.

[Configuration of Image Analyzing Section 320]

FIG. 4 schematically shows one example of the internal configuration ofthe image analyzing section 320. The image analyzing section 320includes a learning processing section 410, a position calculatingsection 420, a lawn recognizing section 430 and a judgment processingsection 440. The judgment processing section 440 for example has a lawnstate judging section 442, a blade state judging section 444, aparameter generating section 446 and a map generating section 448. Eachsection of the image analyzing section 320 may transmit and receiveinformation to and from each other, in directions not limited by arrowsin the figure.

The position calculating section 420 may be one example of a positionalinformation acquiring section. The lawn recognizing section 430 may beone example of a form recognizing section. The judgment processingsection 440 may be one example of an information processing device orcontrol device. The judgment processing section 440 may be one exampleof an image acquiring section, positional information acquiring section,specification information acquiring section, judging section, formrecognizing section, deciding section or control parameter decidingsection. The lawn state judging section 442 may be one example of afeature recognizing section. The blade state judging section 444 may beone example of a judging section. The parameter generating section 446may be one example of a deciding section or control parameter decidingsection.

In the present embodiment, through machine learning, the learningprocessing section 410 constructs various types of learning models to beutilized in the image analyzing section 320. The learning processingsection 410 may construct a learning model utilizing a deep-learningtechnique. For example, the learning processing section 410 constructs alearning model utilizing learning data stored in the information storagesection 322. The learning processing section 410 may store theconstructed learning model in the information storage section 322.Thereby, the lawn recognizing section 430 or judgment processing section440 can execute an image recognition process utilizing the learningmodel constructed by the learning processing section 410.

The learning processing section 410 may separate a work area of the lawnmower 230 into a plurality of subareas, and construct various types oflearning models for the respective subareas. For example, the learningprocessing section 410 constructs a learning model of each subareautilizing image data of lawn grasses captured in each subarea. Thelearning processing section 410 may construct a learning model of eachsubarea utilizing supervisor data prepared for each subarea. Thelearning processing section 410 may construct a learning model of eachsubarea utilizing a feature about the shapes of lawn grasses extractedby the lawn recognizing section 430 from image data of lawn grasses animage of which has been captured in each subarea.

In the present embodiment, the position calculating section 420acquires, from the receiving section 310, positional informationindicating a position where an image to be a target of analysis by theimage analyzing section 320 was captured. Based on the above-mentionedpositional information, the position calculating section 420 calculatesa position of an object of the above-mentioned image. For example, theposition calculating section 420 calculates a position of lawn grassesin a work area based on positional information associated with imagedata of an image capturing the lawn grasses. Examples of theabove-mentioned positional information may include positionalinformation indicating a position of the monitoring camera 220 thatcaptured the above-mentioned image, positional information indicating aposition of the lawn mower 230 at the time when it captured theabove-mentioned image, and the like. The position calculating section420 may transmit positional information indicating a position of anobject of an image to the lawn state judging section 442.

In one embodiment, the position calculating section 420 calculates apositional relationship between an image-capturing device that capturedan image to be a target of analysis and an object in the image. Theposition calculating section 420 calculates a position of an objectbased on a position of the image-capturing device and theabove-mentioned positional relationship. For example, the positioncalculating section 420 calculates a position of an object in an imagebased on positional information associated with image data, animage-capturing condition of the image, and the geometrical arrangementof an image-capturing device in the lawn mower 230 or garden 30.Examples of the image-capturing condition may include (i) an angle ofview, (ii) at least one of a pan angle, a tilt angle and a roll angle,(iii) a zoom factor and the like. Thereby, a position of an object canbe calculated highly precisely.

In another embodiment, the position calculating section 420 judgeswhether or not an image to be a target of analysis includes an object orregion the position and size of which are known. If an image to be atarget of analysis includes an object or region the position and size ofwhich are known, together with an object to be a target of judgment bythe judgment processing section 440, the position calculating section420 calculates a positional relationship between the above-mentionedobject and the above-mentioned object or region. The positioncalculating section 420 calculates a position of an object based on theposition of the above-mentioned object or region and the above-mentionedpositional relationship. Thereby, a position of the object can becalculated highly precisely.

In the present embodiment, the lawn recognizing section 430 acquires,from the receiving section 310, image data of an image to be a target ofanalysis by the image analyzing section 320. The lawn recognizingsection 430 may acquire, from the receiving section 310, positionalinformation indicating a position where the above-mentioned image wascaptured. The lawn recognizing section 430 may acquire, from theposition calculating section 420, positional information indicating aposition of an object in the above-mentioned image. In the presentembodiment, utilizing an image recognition technique, the lawnrecognizing section 430 determines whether or not an image to be atarget of analysis includes a target of judgment by the judgmentprocessing section 440. The target of judgment may be an object in animage or a background in an image. There may be one or more targets ofjudgment.

If an image to be a target of analysis includes a target of judgment bythe judgment processing section 440, the lawn recognizing section 430recognizes the target of judgment by the judgment processing section440, and extracts, from the image to be the target of analysis, at leastone of (i) an image of the target of judgment, (ii) an outline or shapeof the target of judgment and (iii) a feature of the target of judgment.The lawn recognizing section 430 transmits, to the lawn state judgingsection 442, information about an image, outline, shape, feature and thelike of the target of judgment by the judgment processing section 440.The lawn recognizing section 430 may transmit, to the lawn state judgingsection 442 and in association with each other, (i) information about animage, outline, shape, feature and the like of the target of judgment bythe judgment processing section 440 and (ii) positional informationindicating a position where the image to be the target of analysis wascaptured or a position of an object in the image.

The lawn recognizing section 430 may store, in the information storagesection 322, image data acquired from the receiving section 310. Thelawn recognizing section 430 may store, in the information storagesection 322, information about an image, outline, shape, feature and thelike of the target of judgment by the judgment processing section 440.The lawn recognizing section 430 may store, in the information storagesection 322 and in association with each other, the above-mentioned dataimage or above-mentioned information, and positional informationindicating a position where the image was captured or a position of anobject in the image. The lawn recognizing section 430 may store, in theinformation storage section 322, the above-mentioned data image orabove-mentioned information as learning data of the learning processingsection 410.

In the present embodiment, the lawn recognizing section 430 recognizesthe form of lawn grasses present in an image (which may be sometimesreferred to as lawn grasses included in an image). The lawn recognizingsection 430 recognizes the form of at least one lawn grass among one ormore lawn grasses present in an image. The lawn grasses may be oneexample of a target of judgment by the judgment processing section 440.For example, the lawn recognizing section 430 analyzes an image acquiredfrom the receiving section 310, and recognizes at least either (i) theshapes of lawn grasses or (ii) end portions of lawn grasses that areincluded in the image.

As explained using FIG. 1, in one embodiment, the lawn recognizingsection 430 recognizes the respective shapes of a plurality of lawngrasses. The lawn recognizing section 430 may treat the entire image asa target, and recognize the respective shapes of a plurality of lawngrasses included in the image. The lawn recognizing section 430 maytreat a partial region of an image as a target, and recognize therespective shapes of one or more lawn grasses included in the region.Examples of the above-mentioned region may include a focused region, aregion that satisfies a condition about colors and the like. The lawnrecognizing section 430 may recognize end portions of recognized lawngrasses based on the shapes of the lawn grasses.

In another embodiment, the lawn recognizing section 430 extracts, fromwithin an image, a region that is likely to include many end portions ofa plurality of lawn grasses. For example, the lawn recognizing section430 extracts, as a region that is likely to include many end portions ofa plurality of lawn grasses, one of a plurality of images that areobtained by dividing, in the vertical direction, an image laterallycapturing the plurality of lawn grasses. Thereby, the lawn recognizingsection 430 can recognize end portions of a plurality of lawn grasseswithout recognizing the respective shapes of the lawn grasses. As aresult, depending on images, time required to recognize end portions oflawn grasses can be shortened significantly.

The lawn recognizing section 430 (i) may recognize the form of lawngrasses based on a predetermined determination criterion or algorithm,or (ii) may recognize the form of lawn grasses utilizing a learningmodel obtained through machine learning. The above-mentioneddetermination criterion may be a general criterion for extracting anoutline of an object, or information indicating a condition about eachamong one or more factors to consider to be used for extracting theshapes or end portions of lawn grasses.

Based on what kind of determination criterion the form of lawn grassesis judged may be decided by a user or administrator, or may be decidedthrough machine learning. Based on what kind of determination criterionthe form of lawn grasses is judged may be decided for each type of lawngrasses, or may be decided for each specification of the lawn mower 230.A threshold about the above-mentioned determination criterion may bedecided by a user or administrator, or may be decided through machinelearning. The above-mentioned threshold may be decided for each type oflawn grasses, or may be decided for each specification of the lawn mower230.

For example, the lawn recognizing section 430 recognizes the form oflawn grasses utilizing a learning model constructed by the learningprocessing section 410. The lawn recognizing section 430 may acquirespecification information of the lawn mower 230, and select a learningmodel matching a specification of the lawn mower 230. The lawnrecognizing section 430 may select a learning model matching the type oflawn grasses. For example, the lawn recognizing section 430 acquires mapinformation about vegetation of the garden 30, and estimates the type oflawn grasses captured. The lawn recognizing section 430 may estimate thetype of lawn grasses captured, based on positional informationindicating a position where an image to be a target of analysis wascaptured or a position of an object in the image, and theabove-mentioned map information.

The lawn recognizing section 430 may execute the above-mentioned processfor each piece among a plurality of pieces of image data received by thereceiving section 310. The lawn recognizing section 430 may separate awork area of the lawn mower 230 into a plurality of subareas, andexecute the above-mentioned recognition process for each subarea. Thelawn recognizing section 430 may execute the above-mentioned recognitionprocess for a predetermined number of pieces of image data for eachsubarea.

[Outline of Judgment Processing Section 440]

In the present embodiment, the judgment processing section 440 executesvarious types of judgment processes. The judgment processing section 440may execute the judgment processes utilizing information indicating theform of lawn grasses recognized by the lawn recognizing section 430.Thereby, judgment precision can be improved. In one embodiment, thejudgment processing section 440 executes the judgment processes based ona predetermined determination criterion. In another embodiment, thejudgment processing section 440 executes the judgment processesutilizing a learning model constructed by the learning processingsection 410. The judgment processing section 440 may transmit a resultof judgment to the instruction generating section 330 or transmittingsection 340.

According to one embodiment, the judgment processing section 440 firstjudges the state of lawn grasses. Next, the judgment processing section440 judges the state of the lawn mower 230 based on a result of judgmentabout the lawn grasses. The judgment processing section 440 may generatea state parameter indicating the state of the lawn mower 230. Accordingto another embodiment, the judgment processing section 440 first judgesthe state of lawn grasses. Next, the judgment processing section 440generates a control parameter based on a result of judgment about lawngrasses. According to still another embodiment, the judgment processingsection 440 first judges the state of lawn grasses. Next, the judgmentprocessing section 440 generates a water-supply parameter based on aresult of judgment about lawn grasses. According to still anotherembodiment, the judgment processing section 440 generates mapinformation about various types of parameters. The judgment processingsection 440 may generate map information about vegetation in the garden30.

[Outline of Lawn State Judging Section 442]

In the present embodiment, the lawn state judging section 442 judges thestate of lawn grasses based on image data of the lawn grasses acquiredby the receiving section 310. For example, the lawn state judgingsection 442 acquires, from the lawn recognizing section 430, informationabout the form of lawn grasses recognized by the lawn recognizingsection 430. The lawn state judging section 442 judges the state of lawngrasses based on the information about the form of the lawn grasses.

The lawn state judging section 442 (i) may judge the state of lawngrasses based on a predetermined determination criterion, or (ii) mayjudge the state of lawn grasses utilizing a learning model obtainedthrough machine learning. The above-mentioned determination criterionmay be information in which one or more factors to consider, conditionsabout respective factors to consider and the state of lawn grasses areassociated with each other. Examples of the factors to consider forjudging the state of lawn grasses may include (i) the type of lawngrasses, (ii) the number or density of lawn grasses, (iii) the shapes oflawn grasses, (iv) the appearance of end portions of lawn grasses, (v) aspecification of the lawn mower 230, and the like.

Based on what kind of determination criterion the state of lawn grassesis judged may be decided by a user or administrator, or may be decidedthrough machine learning. Based on what kind of determination criterionthe state of lawn grasses is judged may be decided for each type of lawngrasses, or may be decided for each specification of the lawn mower 230.A threshold for deciding whether or not a target of judgment matches acondition about each factor to consider may be decided by a user oradministrator, or may be decided through machine learning. Theabove-mentioned threshold may be decided for each type of lawn grasses,or may be decided for each specification of the lawn mower 230.

The state of lawn grasses may be evaluated by consecutive numericalvalues, or may be evaluated stepwise using a plurality of steps.Examples of the state of lawn grasses may include the cut state of lawngrasses, the growth state of lawn grasses and the like. The cut state oflawn grasses may be the state of cut surfaces. Examples of the growthstate of lawn grasses may include the type of lawn grasses, the densityof lawn grasses, whether the growth is good or bad, sufficiency orinsufficiency of lawn mowing, sufficiency or insufficiency of water,sufficiency or insufficiency of nutriment and the like. Examples of aparameter indicating sufficiency or insufficiency of water may includeat least one of (a) the level of water content in a medium of lawngrasses, (b) whether water-supply to lawn grasses is necessary or notand (c) the amount of water-supply to lawn grasses.

The lawn state judging section 442 transmits a result of judgment aboutthe state of lawn grasses for example to at least either the blade statejudging section 444 or the parameter generating section 446. The lawnstate judging section 442 may transmit, to at least either the bladestate judging section 444 or the parameter generating section 446 and inassociation with each other, a result of judgment about the state oflawn grasses and the positional information of the lawn grasses.

In one embodiment, the lawn state judging section 442 receives an inputof information indicating the form of lawn grasses, and outputs theinformation indicating the state of the lawn grasses. In anotherembodiment, the lawn state judging section 442 recognizes a feature oflawn grasses based on the form of the lawn grasses, and judges the stateof the lawn grasses based on the feature. In still another embodiment,the lawn state judging section 442 recognizes a feature of end portionsof lawn grasses based on the form of the lawn grasses, and judges thestate of the lawn grasses based on the feature.

The lawn state judging section 442 (i) may recognize a feature of lawngrasses or a feature of end portions of the lawn grasses based on apredetermined determination criterion, or (ii) may recognize a featureof lawn grasses or a feature of end portions of the lawn grassesutilizing a learning model obtained through machine learning. Theabove-mentioned determination criterion may be information in which oneor more factors to consider, a condition about respective factors toconsider and features of lawn grasses or particular features of endportions of the lawn grasses are associated with each other. Examples ofthe factors to consider for judging a feature of lawn grasses or afeature of end portions of the lawn grasses may include (i) the type oflawn grasses, (ii) the number or density of lawn grasses, (iii) theshapes of lawn grasses, (iv) the appearance of end portions of lawngrasses, (v) a specification of the lawn mower 230, and the like.

Based on what kind of determination criterion a feature of lawn grassesor a feature of end portions of the lawn grasses is judged may bedecided by a user or administrator, or may be decided through machinelearning. Based on what kind of determination criterion a feature oflawn grasses or a feature of end portions of the lawn grasses is judgedmay be decided for each type of lawn grasses, or may be decided for eachspecification of the lawn mower 230. A threshold for deciding whether ornot a target of judgment matches a condition about each factor toconsider may be decided by a user or administrator, or may be decidedthrough machine learning. The above-mentioned threshold may be decidedfor each type of lawn grasses. The above-mentioned threshold may bedecided for each specification of the lawn mower 230.

Examples of features of lawn grasses may include at least one of (i) thetype of lawn grasses, (ii) the number or density of lawn grasses, (iii)the shapes of lawn grasses, and (iv) inclination of lawn grasses to amedium. Examples of features of end portions of lawn grasses may include(i) at least one of the shape, hue and luster of end portions of lawngrasses, (ii) a difference between end portions of lawn grasses andanother portion of the lawn grasses, and the like. If lawn grasses arecut, a feature of end portions of the lawn grasses may be a feature ofcut portions. Examples of features of cut portions may include (i) atleast one of the shape, hue and luster of cut surfaces, (ii) presence orabsence, or degree of burrs, (iii) presence or absence, or degree ofliquid droplets, and the like.

The lawn state judging section 442 may analyze image data of lawngrasses present in the forward direction in terms of a course of thelawn mower 230, and recognizes a feature of the lawn grasses. The lawnstate judging section 442 may analyze image data of lawn grasses presentin a region that the lawn mower 230 passed through, and recognize afeature of the lawn grasses. Also, the lawn state judging section 442may analyze image data of lawn grasses present in a region that the lawnmower 230 passed through, and recognize a feature of cut portions.

In still another embodiment, the lawn state judging section 442 mayacquire, from the lawn mower 230, information about an electric currentvalue of a motor to rotate a blade. The lawn state judging section 442may recognize a feature of lawn grasses based on an electric currentvalue of a motor to rotate a blade. If the lawn mower 230 cuts a hardmaterial, an electric current value of a motor to rotate a bladeincreases. Also, the hardness of lawn grasses varies depending on thetypes of lawn grasses. Because of this, an electric current value of amotor can be a factor to consider for judging the type of lawn grasses.The lawn state judging section 442 may recognize a feature of lawngrasses based on image data of the lawn grasses present in the forwarddirection in terms of a course of the lawn mower 230 and an electriccurrent value of a motor to rotate a blade. For example, the lawn statejudging section 442 decides the density of lawn grasses based on aresult of image analysis, and decides the hardness of the lawn grassesbased on the density of the lawn grasses and an electric current valueof a motor.

[Outline of Process at Lawn State Judging Section 442]

For example, the lawn state judging section 442 first receives, from thelawn recognizing section 430, information indicating the form of lawngrasses recognized by the lawn recognizing section 430. The lawn statejudging section 442 may receive, from the lawn recognizing section 430,image data of an image to be a target of analysis. The lawn statejudging section 442 may acquire, from the position calculating section420 or lawn recognizing section 430, positional information indicating aposition where an image of lawn grasses was captured or a position oflawn grasses (which may be sometimes referred to as lawn grasspositional information).

Next, the lawn state judging section 442 recognizes a feature of lawngrasses or a feature of end portions of the lawn grasses based oninformation indicating the form of the lawn grasses. A feature of lawngrasses that should be recognized may be any feature as long as it isutilized in a judgment process at the lawn state judging section 442,and specific details are not particularly limited. The lawn statejudging section 442 for example recognizes a feature of lawn grasses ora feature of end portions of the lawn grasses utilizing a learning modelconstructed by the learning processing section 410.

In one embodiment, the lawn state judging section 442 recognizes afeature of lawn grasses or a feature of end portions of the lawn grassesutilizing information indicating the shapes of the lawn grasses. Forexample, utilizing information indicating the shapes of lawn grasses,the lawn state judging section 442 extracts an image of each lawn grassfrom an image capturing a plurality of lawn grasses. Then, the lawnstate judging section 442 recognizes, about at least one lawn grass:presence or absence of a shape that is unique to each type of lawngrasses; thickness; curvature; inclination angle to a medium; shape ofan end portion; color of an end portion; luster of an end portion;whether or not there is variation in colors between end portions andother portions, and details of the variation; and the like.

In another embodiment, the lawn state judging section 442 recognizes afeature of end portions of lawn grasses utilizing information indicatingthe end portions of the lawn grasses. For example, the lawn statejudging section 442 acquires, from the lawn recognizing section 430 andas information indicating end portions of lawn grasses, an image that islikely to include many end portions of lawn grasses. The lawn statejudging section 442 may recognize a feature of the image as a feature ofend portions of lawn grasses. The lawn state judging section 442 mayacquire, from the lawn recognizing section 430 and as an referenceimage, an image that is likely to include many root portions or middleportions of lawn grasses. The lawn state judging section 442 mayrecognize, as a feature of end portions of lawn grasses, a differencebetween a feature of an image that is likely to include many endportions of lawn grasses and a feature of the reference image.

Next, the lawn state judging section 442 judges the state of lawngrasses. For example, the lawn state judging section 442 judges the cutstate of lawn grasses based on a feature of end portions of the lawngrasses. The lawn state judging section 442 may judge the growth stateof lawn grasses based on at least either a feature of the lawn grassesor a feature of end portions of the lawn grasses. Judgment processesabout at least either the cut state or growth state of lawn grasses maybe executed utilizing a learning model constructed by the learningprocessing section 410.

Next, the lawn state judging section 442 outputs a result of judgmentabout the state of lawn grasses. The lawn state judging section 442 mayoutput, in association with each other, a result of judgment about thestate of lawn grasses and positional information of the lawn grasses. Inone embodiment, the lawn state judging section 442 transmits, to theblade state judging section 444, a result of judgment about the cutstate of lawn grasses. Thereby, the blade state judging section 444 canjudge the state of a blade utilizing a result of judgment about the cutstate of lawn grasses.

In another embodiment, the lawn state judging section 442 transmits, tothe parameter generating section 446, a result of judgment about thegrowth state of lawn grasses. Thereby, the parameter generating section446 can generate at least either a control parameter or a water-supplyparameter utilizing a result of judgment about the growth state of lawngrasses. As mentioned below, the parameter generating section 446 maygenerate a control parameter utilizing a result of judgment about thestate of a blade.

The lawn state judging section 442 may execute the above-mentionedprocess for each piece among a plurality of pieces of image datareceived by the receiving section 310. The lawn state judging section442 may separate a work area of the lawn mower 230 into a plurality ofsubareas, and execute the above-mentioned judgment process for eachsubarea. The lawn state judging section 442 may execute theabove-mentioned judgment process for a predetermined number of pieces ofimage data for each subarea.

[Outline of Blade State Judging Section 444]

In the present embodiment, the blade state judging section 444 judgesthe state of a blade of the lawn mower 230.

Examples of the state of a blade may include (i) the cutting performanceof the blade, (ii) whether maintenance of or a check on the blade isnecessary or not, (iii) recommended timing of maintenance of or a checkon the blade, or time left until the timing, and the like. The state ofthe blade may be evaluated by consecutive numerical values, or may beevaluated stepwise using a plurality of steps.

In the present embodiment, the blade state judging section 444 judgesthe state of the blade of the lawn mower 230 based on image dataacquired by the receiving section 310. For example, the blade statejudging section 444 receives a result of judgment about the cut state oflawn grasses from the lawn state judging section 442, and judges thestate of the blade of the lawn mower 230 based on the result ofjudgment. The blade state judging section 444 transmits the result ofjudgment about the state of the blade for example to at least either theparameter generating section 446 or the map generating section 448. Theblade state judging section 444 may transmit, in association with eachother, the result of judgment about the state of the blade andpositional information of the lawn grasses utilized for the judgment toat least either the parameter generating section 446 or the mapgenerating section 448.

The blade state judging section 444 (i) may judge the state of the bladebased on a predetermined determination criterion, or (ii) may judge thestate of the blade utilizing a learning model obtained through machinelearning. The above-mentioned determination criterion may be informationin which one or more factors to consider, conditions about respectivefactors to consider and the state of lawn grasses are associated witheach other. Examples of the factors to consider for judging the state ofa blade may include (i) the type of lawn grasses, (ii) at least one ofthe shape, hue and luster of cut surfaces of lawn grasses, (iii) aspecification of the blade and the like.

Based on what kind of determination criterion the state of the blade isjudged may be decided by a user or administrator, or may be decidedthrough machine learning. Based on what kind of determination criterionthe state of the blade is judged may be decided for each type of lawngrasses, or may be decided for each specification of the lawn mower 230.A threshold for deciding whether or not a target of judgment matches acondition about each factor to consider may be decided by a user oradministrator, or may be decided through machine learning. Theabove-mentioned threshold may be decided for each type of lawn grasses.The above-mentioned threshold may be decided for each specification ofthe blade.

For example, the blade state judging section 444 acquires informationabout a specification of the blade, and the blade state judging section444 judges the state of the blade of the lawn mower 230 based on aresult of judgment about the cut state of lawn grasses and theinformation about the specification of the blade. The information aboutthe specification of a blade is stored for example in a storage deviceof the lawn mower 230, the information storage section 322 or the like.Examples of a specification of the blade may include the type of theblade, the quality of the material of the blade, the size of the bladeand the like. Examples of the type of a blade may include a chip saw, anylon cutter, a metal blade and the like.

According to one embodiment, the blade state judging section 444 judgeswhether maintenance of or a check on the blade is necessary or not basedon a result of judgment about the cut state of lawn grasses. Accordingto another embodiment, it judges whether maintenance of or a check onthe blade is necessary or not based on a result of judgment about thecut state of lawn grasses and information about a specification of theblade. According to still another embodiment, the blade state judgingsection 444 may judge whether maintenance of or a check on the blade isnecessary or not based on a result of judgment about the state of theblade.

In the present embodiment explained, the blade state judging section 444receives a result of judgment about the cut state of lawn grasses fromthe lawn state judging section 442. However, the blade state judgingsection 444 is not limited to the present embodiment. In anotherembodiment, the blade state judging section 444 may receive informationindicating a feature of cut portions of lawn grasses from the lawn statejudging section 442. The feature of cut portions of the lawn grasses isobtained for example by the lawn state judging section 442 extracting itfrom image data. In this case, the blade state judging section 444 mayjudge the state of a blade based on a feature of cut portions of lawngrasses.

[Outline of Parameter Generating Section 446]

In the present embodiment, the parameter generating section 446generates various types of parameters. The parameter generating section446 generates at least one of a state parameter, a control parameter anda water-supply parameter based on a result of judgment by at leasteither the lawn state judging section 442 or the blade state judgingsection 444. For example, the parameter generating section 446 transmitsa generated parameter to the map generating section 448. The parametergenerating section 446 may output the generated parameter to theinstruction generating section 330 or transmitting section 340. Theparameter generating section 446 may output, in association with eachother, the parameter and positional information indicating a position atwhich the parameter is applied.

[State Parameter]

In the present embodiment, for example, the parameter generating section446 receives a result of judgment about the state of the blade from theblade state judging section 444. Then, the parameter generating section446 generates a state parameter indicating the state of the blade basedon a result of judgment about the state of the blade. According to thepresent embodiment, the state parameter is generated for example basedon a feature about at least one of (i) the type of lawn grasses, (ii)the number or density of lawn grasses, (iii) the shapes of lawn grasses,and (iv) the appearance of cut lawn grasses. The appearance of cut lawngrasses may be one example of a feature of cut portions of lawn grasses.The state parameter may be generated based on a specification of thelawn mower 230, an electric current value of a motor to rotate the bladeand the like.

[Control Parameter]

In one embodiment, the parameter generating section 446 receives aresult of judgment about the growth state of lawn grasses from the lawnstate judging section 442. Then, the parameter generating section 446generates a control parameter based on a result of judgment about thegrowth state of lawn grasses. In another embodiment, the parametergenerating section 446 receives a result of judgment about the cut stateof lawn grasses from the lawn state judging section 442. Then, theparameter generating section 446 generates a control parameter based ona result of judgment about the cut state of lawn grasses. According tothese embodiments, the control parameter is generated for example basedon a feature about at least one of (i) the type of lawn grasses, (ii)the number or density of lawn grasses, (iii) the shapes of lawn grasses,and (iv) the appearance of cut lawn grasses. The appearance of cut lawngrasses may be one example of a feature of cut portions of lawn grasses.The control parameter may be generated based on a specification of thelawn mower 230, an electric current value of a motor to rotate a bladeand the like. For example, the parameter generating section 446generates a control parameter about a number of revolution of a motor ofthe lawn mower 230 to rotate the blade, a travel speed of the lawn mower230, a travel direction of the lawn mower 230 and the like based on anelectric current value of the motor.

In still another embodiment, the parameter generating section 446receives a result of judgment about the state of the blade from theblade state judging section 444. The parameter generating section 446generates a control parameter based on a result of judgment about thestate of the blade. For example, if the cutting performance of the bladedoes not satisfy a predetermined condition, the parameter generatingsection 446 decides the control parameter such that (i) a travel speedof the lawn mower 230 becomes lower or (ii) a rotational speed of theblade becomes higher, as compared with a case where the cuttingperformance of the blade satisfies the predetermined condition. Theparameter generating section 446 may generate the control parameterbased on a given combination of a result of judgment about the growthstate of lawn grasses, a result of judgment about the cut state of lawngrasses and a result of judgment about the state of the blade.

[Water-Supply Parameter]

For example, the parameter generating section 446 receives a result ofjudgment about the growth state of lawn grasses from the lawn statejudging section 442. The parameter generating section 446 generates awater-supply parameter based on a result of judgment about the growthstate of lawn grasses. According to the present embodiment, thewater-supply parameter is generated for example based on a feature aboutat least one of (i) the type of lawn grasses, (ii) the number or densityof lawn grasses, (iii) the shapes of lawn grasses, and (iv) theappearance of cut lawn grasses. The appearance of cut lawn grasses maybe one example of a feature of cut portions of lawn grasses. Thereby,for example, it can decide at least one of (a) the level of watercontent in a medium of lawn grasses, (b) whether water-supply to lawngrasses is necessary or not, and (c) the amount of water-supply to lawngrasses based on a feature of at least either the shapes of lawn grassesor end portions of lawn grasses. The water-supply parameter may begenerated based on a specification of the lawn mower 230, an electriccurrent value of a motor to rotate a blade, and the like.

[Outline of Map Generating Section 448]

The map generating section 448 generates various types of mapinformation. Map information of each parameter may be one example of theparameter. The map generating section 448 outputs the map informationfor example to the instruction generating section 330 or transmittingsection 340.

In one embodiment, the map generating section 448 receives, from theparameter generating section 446, various types of parameters, andinformation indicating a position at which the parameters are applied.The map generating section 448 generates map information of eachparameter by associating each parameter and information indicating aposition at which the parameter is applied. The map generating section448 may generate the map information utilizing parameters satisfying apredetermined condition.

In another embodiment, the map generating section 448 may acquire, fromat least either the lawn state judging section 442 or the blade statejudging section 444, (i) positional information indicating a positionwhere an image to be a target of judgment was captured or positionalinformation indicating a position of an object in the image and (ii)information indicating a result of judgment about the target ofjudgment. The map generating section 448 may generate map information byassociating the above-mentioned positional information and informationindicating the above-mentioned result of judgment. The map generatingsection 448 may generate map information utilizing a result of judgmentsatisfying a predetermined condition. At least either the lawn statejudging section 442 or the blade state judging section 444 may outputthe above-mentioned information to 448 if a result of judgment satisfiesa predetermined condition.

In still another embodiment, the map generating section 448 acquires,from the lawn state judging section 442, (i) positional informationindicating a position where an image to be a target of judgment wascaptured, or positional information indicating a position of an objectin the image, and (ii) information about at least one of a plant, ananimal, a microorganism, soil and waste that are included in each image.The information about at least one of a plant, an animal, amicroorganism, soil and waste may be information indicating the type ofat least one of a plant, an animal, a microorganism, soil and waste. Themap generating section 448 may generate map information by associatingthe above-mentioned positional information and information indicatingthe above-mentioned result of judgment. Soil may be one example of amedium of a plant.

Processes at each section in the image analyzing section 320 are notlimited to the embodiment explained using FIG. 4. In another embodiment,at least part of information processing at a particular member of theimage analyzing section 320 may be executed at another member. Forexample, in the present embodiment explained, the lawn recognizingsection 430 recognizes the form of lawn grasses, and the lawn statejudging section 442 judges the state of the lawn grasses based on theform of the lawn grasses. However, the judgment processing section 440is not limited to the present embodiment. In another embodiment, atleast part of information processing at the lawn recognizing section 430may be executed at the lawn state judging section 442.

Also, in the present embodiment explained, the parameter generatingsection 446 generates various types of parameters. However, the judgmentprocessing section 440 is not limited to the present embodiment. Inanother embodiment, at least either the lawn state judging section 442or the blade state judging section 444 may generate parameters. Forexample, in the present embodiment explained, the lawn state judgingsection 442 judges the growth state of lawn grasses based on the form ofthe lawn grasses, and the parameter generating section 446 generates awater-supply parameter based on a result of judgment about the growthstate of the lawn grasses. However, the judgment processing section 440is not limited to the present embodiment. In another embodiment, thelawn state judging section 442 may generate a water-supply parameterbased on the form of lawn grasses. For example, based on the form oflawn grasses, the lawn state judging section 442 decides at least one of(a) the level of water content in a medium of lawn grasses, (b) whetherwater-supply to lawn grasses is necessary or not, and (c) the amount ofwater-supply to lawn grasses.

FIG. 5 schematically shows one example of the internal configuration ofthe information storage section 322. In the present embodiment, theinformation storage section 322 includes a work machine informationstorage section 510, a learning data storage section 520 and a learningmodel storage section 530. The learning data storage section 520 may beone example of a shape information storage section.

The work machine information storage section 510 stores informationabout a specification of the lawn mower 230. The learning data storagesection 520 stores learning data of the learning processing section 410.The learning model storage section 530 stores learning datacorresponding to various conditions. The learning data storage section520 may store, in association with each other, (i) positionalinformation acquired by the position calculating section 420 and (ii)information about the shapes of lawn grasses recognized by the lawnrecognizing section 430. The learning model storage section 530 stores alearning model constructed by the learning processing section 410. Thelearning model storage section 530 may store learning modelscorresponding to various conditions.

[Outline of Lawn Mower 230]

The outline of the lawn mower 230 is explained using FIG. 6, FIG. 7 andFIG. 8. FIG. 6 schematically shows one example of the internalconfiguration of the lawn mower 230. In the present embodiment, the lawnmower 230 includes a housing 602. In the present embodiment, the lawnmower 230 includes, under the housing 602, a pair of front wheels 612and a pair of rear wheels 614. The lawn mower 230 may include a pair ofmotors for run 616 that respectively drive the pair of rear wheels 614.

In the present embodiment, the lawn mower 230 includes a work unit 620.The work unit 620 for example has a blade disk 622, a cutter blade 624,a motor for work 626 and a shaft 628. The lawn mower 230 may include aposition adjusting section 630 that adjusts a position of the work unit620. The work unit 620 may be one example of a cutting section. Theblade disk 622 and the cutter blade 624 may be one example of a rotorfor cutting a work target.

The blade disk 622 is coupled with the motor for work 626 via the shaft628. The cutter blade 624 may be a cutting blade for cutting lawngrasses. The cutter blade 624 is attached to the blade disk 622 androtates together with the blade disk 622. The motor for work 626 rotatesthe blade disk 622.

In the present embodiment, inside the housing 602 or above the housing602, the lawn mower 230 includes a battery unit 640, a user interface650, an image-capturing unit 660, a sensor unit 670 and a control unit680. The image-capturing unit 660 may be one example of animage-capturing section or image acquiring section. The control unit 680may be one example of a judging section, information processing deviceor control device.

In the present embodiment, the battery unit 640 supplies electric powerto each section of the lawn mower 230. In the present embodiment, theuser interface 650 receives a user input. The user interface 650 outputsinformation to a user. Examples of the user interface 650 may include akeyboard, a pointing device, a microphone, a touch panel, a display, aspeaker and the like.

In the present embodiment, the image-capturing unit 660 captures animage of the space around the lawn mower 230. The image-capturing unit660 may capture an image of lawn grasses to be a work target of the lawnmower 230. The image-capturing unit 660 may capture an image of lawngrasses cut by the lawn mower 230. The image-capturing unit 660 mayacquire a still image of an object or acquire a moving image of anobject. The image-capturing unit 660 may have a plurality of imagesensors. The image-capturing unit 660 may be a 360-degree angle camera.

In the present embodiment, the sensor unit 670 includes various types ofsensors. The sensor unit 670 transmits outputs of various types ofsensors to the control unit 680. Examples of the sensors may include aGPS signal receiver, a beacon receiver, a radio field intensitymeasuring machine, an acceleration sensor, an angular speed sensor, awheel speed sensor, a contact sensor, a magnetic sensor, a temperaturesensor, a humidity sensor, a soil water sensor and the like.

In the present embodiment, the control unit 680 controls operation ofthe lawn mower 230. According to one embodiment, the control unit 680controls the pair of motors for run 616 to control travel of the lawnmower 230. According to another embodiment, the control unit 680controls the motor for work 626 to control work of the lawn mower 230.

The control unit 680 may control the lawn mower 230 based on a result ofa judgment process at the image analyzing section 320 of the managingserver 210. For example, the control unit 680 controls the lawn mower230 in accordance with an instruction generated by the instructiongenerating section 330 of the managing server 210.

In another embodiment, the control unit 680 may execute various types ofjudgment processes. The control unit 680 may execute at least one ofjudgment processes at the judgment processing section 440. In oneembodiment, the control unit 680 may control the lawn mower 230 based ona result the above-mentioned judgment processes. For example, thecontrol unit 680 judges the state of the work unit 620 based on imagedata of an image captured by the image-capturing unit 660. The state ofthe work unit 620 may be the cutting performance of the cutter blade624.

In another embodiment, the control unit 680 may control the sprinklingdevice 240 based on a result of the above-mentioned judgment processes.For example, the control unit 680 recognizes the shapes of lawn grassesbased on image data of an image captured by the image-capturing unit660. The control unit 680 decides a water-supply parameter based on theshapes of lawn grasses. The control unit 680 transmits a water-supplyparameter to the sprinkling device 240 to control the amount ofwater-supply to a particular position in the garden 30.

FIG. 7 schematically shows one example of the internal configuration ofthe control unit 680. In the present embodiment, the control unit 680includes a communication control section 710, a running control section720, a work unit control section 730 and an input-output control section740. The communication control section 710 may be one example of anotifying section or image acquiring section. The running controlsection 720 may be one example of a travel control section. The workunit control section 730 may be one example of a work control section.

In the present embodiment, the communication control section 710controls communication with an instrument located outside the lawn mower230. The communication control section 710 may be a communicationinterface compatible with one or more communication systems. Examples ofthe instrument located outside may include the user terminal 20, themanaging server 210, the sprinkling device 240 and the like. Thecommunication control section 710 as necessary may acquire, from themonitoring camera 220, image data of an image captured by the monitoringcamera 220.

In the present embodiment, the running control section 720 controls themotors for run 616 to control travel of the lawn mower 230. The runningcontrol section 720 controls autonomous run of the lawn mower 230. Forexample, the running control section 720 controls at least one of atravel speed, travel direction and travel route of the lawn mower 230.

The running control section 720 may control the motors for run 616 basedon a result of judgment at the image analyzing section 320 of themanaging server 210. In another embodiment, the running control section720 may control the motors for run 616 based on a result of a judgmentprocess at the control unit 680.

In the present embodiment, the work unit control section 730 controlsthe work unit 620. The work unit control section 730 may control atleast one of the type of work, strength of work and schedule of work ofthe work unit 620. For example, the work unit control section 730controls the motor for work 626 to control the strength of work of thework unit 620. The work unit control section 730 may control theposition adjusting section 630 to control the strength of work of thework unit 620.

The work unit control section 730 may control at least either the motorfor work 626 or the position adjusting section 630 based on a result ofjudgment at the image analyzing section 320 of the managing server 210.In another embodiment, the work unit control section 730 may control atleast either the motor for work 626 or the position adjusting section630 based on a result of a judgment process at the control unit 680. Instill another embodiment, the work unit control section 730 may monitoran electric current value of the motor for work 626. The work unitcontrol section 730 may transmit, to the image analyzing section 320,information indicating an electric current value of the motor for work626.

In the present embodiment, the input-output control section 740 receivesan input from at least one of the user interface 650, theimage-capturing unit 660 and the sensor unit 670. The input-outputcontrol section 740 outputs information to the user interface 650. Theinput-output control section 740 may control at least one of the userinterface 650, the image-capturing unit 660 and the sensor unit 670. Forexample, the input-output control section 740 controls at least oneinstrument among the user interface 650, the image-capturing unit 660and the sensor unit 670 by adjusting setting of the instrument.

FIG. 8 schematically shows another example of the internal configurationof the control unit 680. It was explained using FIG. 7 that the managingserver 210 has the image analyzing section 320, the information storagesection 322 and the instruction generating section 330, and varioustypes of judgment processes are executed at the managing server 210. Inthe embodiment of FIG. 7, the control unit 680 controls the lawn mower230 based on a result of a judgment process at the image analyzingsection 320.

The embodiment of the FIG. 8 is different from the embodiment of FIG. 7in that the image analyzing section 320, the information storage section322 and the instruction generating section 330 are disposed in thecontrol unit 680. In the present embodiment, the running control section720 and the work unit control section 730 control at least one of themotors for run 616, the motor for work 626 and the position adjustingsection 630 based on an instruction generated by the instructiongenerating section 330. In other respects, it may have a configurationsimilar to the embodiment of FIG. 7.

In the present embodiment explained, the control unit 680 has the imageanalyzing section 320, the information storage section 322 and theinstruction generating section 330. However, the control unit 680 is notlimited to the present embodiment. In another embodiment, one or twoamong the image analyzing section 320, the information storage section322 and the instruction generating section 330 may be disposed in thecontrol unit 680, and the remaining sections may be disposed in themanaging server 210. For example, the control unit 680 may not have theinformation storage section 322. In this case, the image analyzingsection 320 disposed in the control unit 680 as necessary executes imageanalysis processing by accessing the information storage section 322disposed in the managing server 210.

In still another embodiment, some configurations among a plurality ofconfigurations included in the image analyzing section 320 may bedisposed in the control unit 680 and the remaining configurations may bedisposed in the managing server 210. For example, among a plurality ofconfigurations included in the image analyzing section 320, the judgmentprocessing section 440 is disposed in the control unit 680, and thelearning processing section 410, the position calculating section 420and the lawn recognizing section 430 are disposed in the managing server210. Also, the parameter generating section 446 may be disposed in thecontrol unit 680, and the remaining configurations of the imageanalyzing section 320 may be disposed in the managing server 210.

FIG. 9 schematically shows one example of the system configuration ofthe sprinkling device 240. In the present embodiment, the sprinklingdevice 240 includes the sprinkler 242 and the water-supply controlsection 244. The sprinkling device 240 may include a hydrant 902 and awater-supply line 904. The water-supply line 904 may be a pipe orwater-supply facility that transfers water supplied from the hydrant 902to the sprinkler 242. One end of the water-supply line 904 may beconnected to the hydrant 902, and the other end of the water-supply line904 may be connected to the sprinkler 242.

In the present embodiment, the water-supply control section 244 isdisposed between the sprinkler 242 and the hydrant 902 in thewater-supply line 904, and adjusts the amount of water-supply to thesprinkler 242. In the present embodiment, the water-supply controlsection 244 has an automatic valve 910, a communication control section920 and an open-close control section 930.

In the present embodiment, the automatic valve 910 adjusts the amount ofwater to flow through the water-supply line 904. The automatic valve 910may adjust the amount of water to flow through the water-supply line 904based on a control signal from the open-close control section 930. Theautomatic valve 910 may be an electrically operated valve.

In the present embodiment, the communication control section 920controls communication with the managing server 210 or the lawn mower230. The communication control section 920 receives, from the managingserver 210 or lawn mower 230, a water-supply parameter or water-supplyinstruction. The communication control section 920 transmits thewater-supply parameter or water-supply instruction to the open-closecontrol section 930. In the present embodiment, the open-close controlsection 930 controls operation of the automatic valve 910. For example,the open-close control section 930 controls operation of the automaticvalve 910 based on a water-supply parameter or water-supply instruction.

FIG. 10 schematically shows another example of the system configurationof the sprinkling device 240. In the present embodiment, the sprinklingdevice 240 includes the sprinkler 242 and the water-supply controlsection 244. The sprinkling device 240 may include the water-supply line904 and a water tank 1002. In the present embodiment, the water-supplycontrol section 244 has a pump 1010, the communication control section920 and a pump control section 1030. The sprinkling device 240 may bemounted on the lawn mower 230.

In the present embodiment, one end of the water-supply line 904 isconnected to the water tank 1002, and the other end of the water-supplyline 904 is connected to the sprinkler 242. The pump 1010 transferswater inside the water tank 1002. The pump control section 1030 mayadjust the amount of water to be transferred, based on a control signalfrom the pump control section 1030. The pump control section 1030controls operation of the pump 1010. For example, the pump controlsection 1030 controls operation of the pump control section 1030 basedon a water-supply parameter or water-supply instruction received by thecommunication control section 920.

FIG. 11 schematically shows one example of information processing at theimage analyzing section 320. According to the present embodiment, atStep 1102 (Step may be sometimes abbreviated to S), the lawn recognizingsection 430 acquires, from the receiving section 310, image data of animage to be a target of analysis. At S1104, the lawn recognizing section430 determines whether or not the image acquired from the receivingsection 310 is an image after lawn mowing or an image before lawnmowing. For example, if the image acquired from the receiving section310 is an image of lawn grasses present in the forward direction interms of a course of the lawn mower 230, the lawn recognizing section430 determines that the image is an image before lawn mowing. If theimage acquired from the receiving section 310 is an image of lawngrasses present in a region that the lawn mower 230 passed through, thelawn recognizing section 430 determines that the image is an image afterlawn mowing.

In one embodiment, the lawn recognizing section 430 may acquireinformation about at least either an installation position or animage-capturing condition of an image-capturing device that captured theimage, and based on the information, determine whether the imageacquired from the receiving section 310 is an image of lawn grassespresent in the forward direction in terms of a course of the lawn mower230 or an image of lawn grasses present in a region that the lawn mower230 passed through. For example, if the image-capturing device ismounted at a front portion of the lawn mower 230, an image-capturingdevice is set to capture an image of the forward direction of the lawnmower 230, and so on, the lawn recognizing section 430 determines thatan image acquired from the receiving section 310 is an image of lawngrasses present in the forward direction in terms of a course of thelawn mower 230.

In another embodiment, the lawn recognizing section 430 acquires, fromthe position calculating section 420, positional information indicatinga position of lawn grasses in an image. The position calculating section420 decides a position of lawn grasses in an image for example based onat least either an installation position or an image-capturing conditionof an image-capturing device that captured the image. The lawnrecognizing section 430 acquires information indicating the currentposition of the lawn mower 230. The lawn recognizing section 430 maydetermine whether an image acquired from the receiving section 310 is animage of lawn grasses present in the forward direction in terms of acourse of the lawn mower 230 or an image of lawn grasses present in aregion that the lawn mower 230 passed through, based on a position ofthe lawn grasses and a position of the lawn mower 230.

If it is determined that the image acquired from the receiving section310 is an image after lawn mowing (if YES at S1104), at S1112, the lawnrecognizing section 430 recognizes end portions of one or more lawngrasses present in the image. After recognizing the shape of at leastone lawn grass, the lawn recognizing section 430 may recognize an endportion of the lawn grass based on the shape of the lawn grass. Also,the lawn state judging section 442 judges the cut state of the lawngrass based on a feature of the end portion of the lawn grass.

Next, at S1114, the lawn state judging section 442 transmits, to theblade state judging section 444, information indicating the cut state ofthe lawn grass. Then, the blade state judging section 444 judges thestate of a blade based on the cut state of the lawn grass. Also, atS1116, the lawn state judging section 442 judges the growth state of thelawn grass based on a feature of the end portion of the lawn grass. Inthis case, the lawn recognizing section 430 may recognize the shape oflawn grass, and the lawn state judging section 442 may judge the growthstate of the lawn grass based on the shape of the lawn grass.

Thereafter, at S1122, the parameter generating section 446 generatesvarious types of parameters based on at least one of the cut state ofthe lawn grass, the growth state of the lawn grass and the state of ablade. In this case, the map generating section 448 may generate mapinformation utilizing the parameters generated by the parametergenerating section 446.

On the other hand, if it is determined that the image acquired from thereceiving section 310 is an image before lawn mowing (if NO at S1104),at S1116, the lawn state judging section 442 judges the growth state oflawn grasses by a procedure similar to the above-mentioned one, and atS1122, the parameter generating section 446 generates various types ofparameters by a procedure similar to the above-mentioned one. The mapgenerating section 448 may generate map information utilizing parametersgenerated by the parameter generating section 446.

The various types of parameters generated at S1122 are transmitted tothe instruction generating section 330. The various types of parametersmay be transmitted to the instruction generating section 330 in a mapinformation format. Thereby, processes at the image analyzing section320 end. The image analyzing section 320 (i) may execute a series ofprocesses every time it acquires image data from the receiving section310 or (ii) may execute a series of processes for each subarea and for apredetermined number of pieces of image data.

FIG. 12 schematically shows one example of a data table 1200. The datatable 1200 may be one example of a determination criterion for judgingthe type of lawn grasses. In the present embodiment, the data table 1200utilizes a color of lawn grasses 1202 and a hardness of lawn grasses1204 as factors to consider for judging the type of lawn grasses. Thedata table 1200 stores, in association with each other, a conditionabout the color of lawn grasses 1202 and a condition about the hardnessof lawn grasses 1204, and a result of judgment 1206.

In the present embodiment, the condition about the color of lawn grasses1202 is evaluated using evaluation categories consisting of four steps,“reddish brown”, “yellowish green”, “green” and “dark green”. The colorof lawn grasses may be evaluated based on an image to be a target ofanalysis by the image analyzing section 320. In the present embodiment,the condition about the hardness of lawn grasses 1204 is evaluated usingevaluation categories consisting of three steps, “hard”, “normal” and“soft”. The hardness of lawn grasses 1204 may be evaluated for examplebased on an electric current value of the motor for work 626. Thresholdsfor classification into respective evaluation categories may be decidedby a user or administrator, or may be decided through machine learning.

FIG. 13 schematically shows one example of a data table 1300. The datatable 1300 may be one example of a determination criterion for decidinga determination criterion to be utilized in various types of judgmentprocesses. In the present embodiment, the data table 1300 stores, inassociation with each other, a type of lawn grasses 1302, adetermination criterion 1304 to be utilized in a process of judging thestate of lawn grasses, and a determination criterion 1306 to be utilizedin a process of judging the state of a blade.

FIG. 14 schematically shows one example of a data table 1400. The datatable 1400 may be one example of a determination criterion to beutilized in a process of judging the state of lawn grasses. In thepresent embodiment, the data table 1400 utilizes, as factors to considerfor judging the state of lawn grasses, shapes of cut portions 1402 and acolor of cut portions 1404. The data table 1400 stores, in associationwith each other, a condition about the shapes of cut portions 1402 and acondition about a color of cut portions 1404, and a result of judgment1406.

While the embodiments of the present invention have been described, thetechnical scope of the invention is not limited to the above describedembodiments. It is apparent to persons skilled in the art that variousalterations and improvements can be added to the above-describedembodiments. Also, matters explained with reference to a particularembodiment can be applied to other embodiments as long as suchapplication does not cause a technical contradiction. For example,matters explained about the embodiment of FIG. 1 can be applied to theembodiments of FIG. 2 to FIG. 14. It is also apparent from the scope ofthe claims that the embodiments added with such alterations orimprovements can be included in the technical scope of the invention.

The operations, procedures, steps, and stages of each process performedby an apparatus, system, program, and method shown in the claims,embodiments, or diagrams can be performed at any order as long as theorder is not indicated by “prior to,” “before,” or the like and as longas the output from a previous process is not used in a later process.Even if the process flow is described using phrases such as “first” or“next” in the claims, embodiments, or diagrams, it does not necessarilymean that the process must be performed at this order.

For example, the following matters are described in the presentspecification.

[Item A-1]

A work machine having an autonomous travel function, comprising:

a cutting section that cuts a work target of the work machine;

an image-capturing section that captures an image of the work target cutby the cutting section; and

a judging section that judges a state of the cutting section based onthe image captured by the image-capturing section.

[Item A-2]

The work machine according to Item A-1, wherein the judging sectionjudges whether maintenance of or a check on the cutting section isnecessary or not based on a result of judgment about the state of thecutting section.

[Item A-3]

The work machine according to Item A-1 or Item A-2, further comprising aspecification information acquiring section that acquires specificationinformation about a specification of the cutting section, wherein

the judging section judges the state of the cutting section based on thespecification information acquired by the specification informationacquiring section and the image captured by the image-capturing section.

[Item A-4]

The work machine according to any one of Item A-1 to Item A-3, furthercomprising a notifying section that notifies a result of judgment by thejudging section to a user of the work machine.

[Item A-5]

The work machine according to any one of Item A-1 to Item A-4, furthercomprising a positional information acquiring section that acquirespositional information indicating a position where the image-capturingsection has captured the image, wherein

the judging section outputs, in association with each other, thepositional information acquired by the positional information acquiringsection and information indicating a result of judgment at the positionindicated by the positional information.

[Item A-6]

The work machine according to Item A-5, wherein if a result of judgmentby the judging section satisfies a predetermined condition, the judgingsection outputs, in association with each other, the positionalinformation acquired by the positional information acquiring section andinformation indicating a result of judgment by the judging section atthe position indicated by the positional information.

[Item A-7]

The work machine according to any one of Item A-1 to Item A-6, furthercomprising a travel control section that controls travel of the workmachine based on a result of judgment by the judging section.

[Item A-8]

The work machine according to any one of Item A-1 to Item A-7, furthercomprising a work control section that controls operation of the cuttingsection based on a result of judgment by the judging section.

[Item A-9]

A control device that controls a work machine having an autonomoustravel function, wherein

the work machine has:

a cutting section that cuts a work target of the work machine; and

an image-capturing section that captures an image of the work target cutby the cutting section, and

the control device comprises:

a judging section that judges a state of the cutting section based onthe image captured by the image-capturing section; and

a control section that controls the work machine based on a result ofjudgment by the judging section.

[Item A-10]

A control program for controlling a work machine having an autonomoustravel function, wherein

the work machine has:

a cutting section that cuts a work target of the work machine; and

an image-capturing section that captures an image of the work target cutby the cutting section, and

the control program is a program for causing a computer to execute:

a judgment procedure of judging a state of the cutting section based onthe image captured by the image-capturing section; and

a control procedure of controlling the work machine based on a result ofjudgment by in judgment procedure.

[Item A-11]

The control program according to Item A-10, wherein

the work machine further has a processor, and

the computer is the processor of the work machine.

[Item B-1]

An information processing device comprising:

an image acquiring section that acquires image data of an image of aplant;

a form recognizing section that recognizes at least either (i) a shapeof the plant or (ii) an end portion of the plant, based on the imagedata acquired by the image acquiring section; and

a deciding section that decides at least one of (a) a level of watercontent in a medium of the plant, (b) whether water-supply to the plantis necessary or not, and (c) an amount of water-supply to the plant,based on a feature of at least either the shape of the plant or the endportion of the plant that is recognized by the form recognizing section.

[Item B-2]

The information processing device according to Item B-1, wherein

the deciding section:

recognizes a feature of at least either the shape of the plant or theend portion of the plant, based on a result of recognition by the formrecognizing section; and

decides at least one of (a) the level of water content in the medium ofthe plant, (b) whether water-supply to the plant is necessary or not,and (c) the amount of water-supply to the plant, based on the featurerecognized by the feature recognizing section.

[Item B-3]

The information processing device according to Item B-2, furthercomprising:

a positional information acquiring section that acquires positionalinformation indicating a position where the image has been captured; and

a form information storage section that stores, in association with eachother, (i) the positional information acquired by the positionalinformation acquiring section, and (ii) information about at leasteither the shape of the plant or the end portion of the plant recognizedby the form recognizing section, wherein

the deciding section recognizes a feature of at least either the shapeof the plant or the end portion of the plant, utilizing, as learningdata, information stored in the form information storage section.

[Item B-4]

The information processing device according to Item B-1 or Item B-2,further comprising a positional information acquiring section thatacquires positional information indicating a position where the imagehas been captured, wherein

the deciding section outputs, in association with each other, thepositional information acquired by the positional information acquiringsection and at least one of (a) the level of water content in the mediumof the plant, (b) whether water-supply to the plant is necessary or not,and (c) the amount of water-supply to the plant at the positionindicated by the positional information.

[Item B-5]

The information processing device according to Item B-4, wherein if atleast one of (a) the level of water content in the medium of the plant,(b) whether water-supply to the plant is necessary or not, and (c) theamount of water-supply to the plant satisfies a predetermined condition,the deciding section outputs, in association with each other, thepositional information acquired by the positional information acquiringsection and at least one of (a) the level of water content in the mediumof the plant, (b) whether water-supply to the plant is necessary or not,and (c) the amount of water-supply to the plant at the positionindicated by the positional information.

[Item B-6]

The information processing device according to any one of Item B-1 toItem B-5, wherein the deciding section decides at least either (i)whether water-supply to the plant is necessary or not or (ii) the amountof water-supply, based on the level of water content in the medium ofthe plant.

[Item B-7]

A water-supply system comprising:

the information processing device according to Item B-6; and

a water-supply section that supplies water to the plant based on adecision by the deciding section.

[Item B-8]

The water-supply system according to Item B-7, further comprising a workmachine having an autonomous travel function, wherein

the work machine has an image-capturing section that captures an imageof the plant, and

an image acquiring section of the information processing device acquiresimage data of the image of the plant captured by the image-capturingsection of the work machine.

[Item B-9]

The water-supply system according to Item B-7, further comprising a workmachine having an autonomous travel function, wherein

the water-supply section is disposed in the work machine.

[Item B-10]

The water-supply system according to Item B-7, further comprising a workmachine having an autonomous travel function, wherein

the work machine has a cutting section that cuts the plant, and

the deciding section of the information processing device decides atleast one of (a) the level of water content in the medium of the plant,(b) whether water-supply to the plant is necessary or not, and (c) theamount of water-supply to the plant, based on a feature of a cut portionof the plant cut by the cutting section.

[Item B-11]

An information processing system comprising:

the information processing device according to any one of Item B-1 toItem B-6; and

a work machine having an autonomous travel function, wherein

the work machine has an image-capturing section that captures an imageof the plant, and

an image acquiring section of the information processing device acquiresimage data of the image of the plant captured by the image-capturingsection of the work machine.

[Item B-12]

An information processing system comprising:

the information processing device according to any one of Item B-1 toItem B-6; and

a work machine having an autonomous travel function, wherein

the work machine has a cutting section that cuts the plant, and

the deciding section of the information processing device decides atleast one of (a) the level of water content in the medium of the plant,(b) whether water-supply to the plant is necessary or not, and (c) theamount of water-supply to the plant, based on a feature of a cut portionof the plant cut by the cutting section.

[Item B-13]

A program for causing a computer to function as the informationprocessing device according to any one of Item B-1 to Item B-6.

[Item C-1]

A control device that controls a work machine having an autonomoustravel function, the control device comprising:

an image acquiring section that acquires image data of an image of awork target of the work machine;

a feature recognizing section that recognizes a feature about at leastone of (i) a type of the work target of the work machine, (ii) a numberor density of the work target, (iii) a shape of the work target and (iv)an appearance of the work target after work, based on the image dataacquired by the image acquiring section; and

a control parameter deciding section that decides at least either (i) aparameter for controlling travel of the work machine or (ii) a parameterfor controlling work of the work machine, based on the featurerecognized by the feature recognizing section.

[Item C-2]

The control device according to Item C-1, further comprising atransmitting section that transmits, to the work machine, the parameterdecided by the control parameter deciding section.

[Item C-3]

The control device according to Item C-1 or Item C-2, wherein

the work machine has a cutting section that cuts the work target,

the feature recognizing section recognizes a feature of a cut portion ofthe work target cut by the cutting section, and

the control device further comprises a judging section that judges astate of the cutting section based on the feature of the cut portion ofthe work target recognized by the feature recognizing section.

[Item C-4]

The control device according to Item C-3, wherein the control parameterdeciding section decides the parameter based on a result of judgment bythe judging section.

[Item C-5]

The control device according to Item C-3 or Item C-4, wherein

the cutting section has a rotor for cutting the work target,

the judging section judges a cutting performance of the cutting section,and

if the cutting performance of the cutting section judged by the judgingsection does not satisfy a predetermined condition, the controlparameter deciding section decides the parameter such that (i) a travelspeed of the work machine becomes lower or (ii) a rotational speed ofthe rotor becomes higher, as compared with a case where the cuttingperformance of the cutting section satisfies the predeterminedcondition.

[Item C-6]

The control device any one of Item C-1 to Item C-5, wherein

the work machine has an image-capturing section that captures an imageof the work target, and

the image acquiring section acquires image data of the image of the worktarget captured by the image-capturing section of the work machine.

[Item C-7]

A work machine having an autonomous travel function, the work machinecomprising;

the control device according to any one of Item C-1 to Item C-5; and

an image-capturing section that captures an image of the work target,wherein

the image acquiring section of the control device acquires image data ofthe image of the work target captured by the image-capturing section ofthe work machine.

[Item C-8]

A program for causing a computer to function as the control deviceaccording to any one of Item C-1 to Item C-6.

What is claimed is:
 1. A work machine having an autonomous travelfunction, comprising: a cutter for cutting a work target of the workmachine; an image-capturing section that captures an image of the worktarget cut by the cutter; and a judging section that judges a state ofthe cutter based on the image captured by the image-capturing section.2. The work machine according to claim 1, wherein the judging sectionjudges whether maintenance of or a check on the cutter is necessary ornot based on a result of judgment about the state of the cutter.
 3. Thework machine according to claim 1, further comprising a specificationinformation acquiring section that acquires specification informationabout a specification of the cutter, wherein the judging section judgesthe state of the cutter based on the specification information acquiredby the specification information acquiring section and the imagecaptured by the image-capturing section.
 4. The work machine accordingto claim 1, further comprising a notifying section that notifies aresult of judgment by the judging section to a user of the work machine.5. The work machine according to claim 1, further comprising apositional information acquiring section that acquires positionalinformation indicating a position where the image-capturing section hascaptured the image, wherein the judging section outputs, in associationwith each other, the positional information acquired by the positionalinformation acquiring section and information indicating a result ofjudgment at the position indicated by the positional information.
 6. Thework machine according to claim 1, further comprising a travel controlsection that controls travel of the work machine based on a result ofjudgment by the judging section.
 7. The work machine according to claim1, further comprising a work control section that controls operation ofthe cutter based on a result of judgment by the judging section.
 8. Acontrol device that controls a work machine having an autonomous travelfunction, wherein the work machine has: a cutter for cutting a worktarget of the work machine; and an image-capturing section that capturesan image of the work target cut by the cutter, and the control devicecomprises: a judging section that judges a state of the cutter based onthe image captured by the image-capturing section; and a control sectionthat controls the work machine based on a result of judgment by thejudging section.
 9. A non-transitory computer readable medium storingthereon a program for controlling a work machine having an autonomoustravel function, wherein the work machine has: a cutter for cutting awork target of the work machine; and an image-capturing section thatcaptures an image of the work target cut by the cutter, and the programcauses a computer to execute, by performing operations: a judgmentprocedure of judging a state of the cutter based on the image capturedby the image-capturing section; and a control procedure of controllingthe work machine based on a result of judgment by in judgment procedure.10. The non-transitory computer readable medium according to claim 9,wherein the work machine further has a processor, and the computer isthe processor of the work machine.
 11. The work machine according toclaim 1, wherein the work target is a plant, the state of the cutterincludes at least one of (i) the cutting performance of the cutter, (ii)whether maintenance of or a check on the cutter is necessary or not and(iii) recommended timing of maintenance of or a check on the cutter, ortime left until the timing, and the judging section judges the state ofthe cutter by analyzing the image based on a predetermined determinationcriterion or a learning model, a factor of which includes at least oneof (i) a characteristic of veins at a cutting surface of the plant, (ii)presence or absence, or degree of burrs at the cutting surface of theplant and (iii) presence or absence, or degree of liquid droplets at thecutting surface of the plant.
 12. The work machine according to claim 5,wherein the state of the cutter includes at least one of (i) the cuttingperformance of the cutter, (ii) whether maintenance of or a check on thecutter is necessary or not and (iii) recommended timing of maintenanceof or a check on the cutter, or time left until the timing.
 13. The workmachine according to claim 6, wherein the state of the cutter includesat least one of (i) the cutting performance of the cutter, (ii) whethermaintenance of or a check on the cutter is necessary or not and (iii)recommended timing of maintenance of or a check on the cutter, or timeleft until the timing, and if a state of the cutter does not satisfy apredetermined condition, the travel control section controls the travelof the work machine such that a travel speed of the work machine becomeslower as compared with a case where the state of the cutter satisfiesthe predetermined condition.
 14. The work machine according to claim 7,wherein the state of the cutter includes at least one of (i) the cuttingperformance of the cutter, (ii) whether maintenance of or a check on thecutter is necessary or not and (iii) recommended timing of maintenanceof or a check on the cutter, or time left until the timing, and if astate of the cutter does not satisfy a predetermined condition, the workcontrol section controls the travel of the work machine such that a workstrength of the work machine is increased as compared with a case wherethe state of the cutter satisfies the predetermined condition.
 15. Thework machine according to claim 1, wherein the cutter includes a blade.16. The control device according to claim 8, wherein the work target isa plant, the state of the cutter includes at least one of (i) thecutting performance of the cutter, (ii) whether maintenance of or acheck on the cutter is necessary or not and (iii) recommended timing ofmaintenance of or a check on the cutter, or time left until the timing,and the judging section judges the state of the cutter by analyzing theimage based on a predetermined determination criterion or a learningmodel, a factor of which includes at least one of (i) a characteristicof veins at a cutting surface of the plant, (ii) presence or absence, ordegree of burrs at the cutting surface of the plant and (iii) presenceor absence, or degree of liquid droplets at the cutting surface of theplant.
 17. The control device according to claim 8, further comprising:a positional information acquiring section that acquires positionalinformation indicating a position where the image-capturing section hascaptured the image; wherein the state of the cutter includes at leastone of (i) the cutting performance of the cutter, (ii) whethermaintenance of or a check on the cutter is necessary or not and (iii)recommended timing of maintenance of or a check on the cutter, or timeleft until the timing, and the judging section outputs, in associationwith each other, the positional information acquired by the positionalinformation acquiring section and information indicating a result ofjudgment at the position indicated by the positional information. 18.The control device according to claim 8, wherein the state of the cutterincludes at least one of (i) the cutting performance of the cutter, (ii)whether maintenance of or a check on the cutter is necessary or not and(iii) recommended timing of maintenance of or a check on the cutter, ortime left until the timing, and the control device further comprises: atravel control section that controls travel of the work machine based ona result of judgment by the judging section; wherein if a state of thecutter does not satisfy a predetermined condition, the travel controlsection controls the travel of the work machine such that a travel speedof the work machine becomes lower as compared with a case where thestate of the cutter satisfies the predetermined condition.
 19. Thecontrol device according to claim 8, wherein the state of the cutterincludes at least one of (i) the cutting performance of the cutter, (ii)whether maintenance of or a check on the cutter is necessary or not and(iii) recommended timing of maintenance of or a check on the cutter, ortime left until the timing, the control device further comprises: a workcontrol section that controls operation of the cutter based on a resultof judgment by the judging section, wherein if a state of the cutterdoes not satisfy a predetermined condition, the work control sectioncontrols the travel of the work machine such that a work strength of thework machine is increased as compared with a case where the state of thecutter satisfies the predetermined condition.
 20. The control deviceaccording to claim 8, wherein the cutter includes a blade.